IQM Radiance comes in two variants: 54 qubits, target for availability is Q3/2024 and 150 qubits, targeted from Q1/2025.

Aiming to pave the way to quantum advantage, using the 150-qubit system as a stepping-stone focusing on high-qualityqubits and gates.

IQM Radiance is designed for businesses, high performance computing centers, data centers and governments.

ESPOO, Finland, Nov. 9, 2023 /PRNewswire/ -- IQM Quantum Computers(IQM), a global leader in building quantum computers, today unveiled its quantum computing platform, "IQM Radiance", aiming to pave the way to quantum advantage within the next years with a 150-qubit quantum system.

IQM Radiance

IQM Radiance offers quantum computing capabilities to businesses and governments and can be deployed in high-performance computing and data centres.

"This is the right moment for businesses to invest and harness quantum advantage as early as possible to gain a competitive edge. IQM Radiance allows enterprises to target real-life use cases, testing applications with the most business potential. High-potential areas include machine learning, cybersecurity, system control, energy grid and route optimisation, drug and chemical research and carbon capture," says Dr. Jan Goetz, CEOand Co-founder of IQM Quantum Computers.

Charting out the path to quantum advantage

IQM Radiance follows the launch of IQM Spark, a quantum computer with a pre-installed 5-qubit quantum processing unit tailored for universities and research institutions for learning and giving users full control of experiments.

Radiance starts as a 54-qubit system, and IQM plans for it to be available in 2024 to provide early adopters with the opportunity to master system operations, integrate systems into existing environments, explore algorithm behaviour, and perform quantum advantage experiments.

In addition, IQM will provide customers the opportunity to upgrade the 54-qubit system to a 150-qubit system in 2025. IQM will continue to support customers on their path to quantum advantage by replacing the initial 150-qubit chips by higher performance chips as soon as these are available. This will enable customers to bring added value to end users for them to solve real-life problems with less computing time, or less power, or by achieving more accurate results, as compared to the best classical device of similar size, weight, and cost.

Story continues

"Radiance will be an enterprise-graded system for which we are optimistic that it will bring quantum utility to some applications even with a relatively modest number of quality qubits. Through the acquisition of IQM Radiance, businesses will gain a significant head start on practical applications and system integration. Our upgrade path allows early adopters to start with a smaller system while receiving a larger system with a significant leap in computing power later," explains Dr. Bjrn Ptter, Head of Product at IQM Quantum Computers.

IQM has already demonstrated its technical capabilities in developing technologies to scale up quantum computers in a successful partnership with institutions such as the VTT Technical Research Centre of Finland, where it delivered a remarkable 20-qubit quantum computer, achieving outstanding results. IQM plans to pilot the delivery of a 54-qubit system to VTT in the second quarter of 2024.

"To meet the needs of our customers, we have a product portfolio with offerings that cover the low- to high-end segment of the market," adds Ptter.

About IQM Quantum Computers:

IQM is a global leader in building quantum computers. IQM provides on-premises quantum computers for supercomputing centres and research labs and offers full access to its hardware. For industrial customers, IQM delivers quantum advantage through a unique application-specific, co-design approach. IQM's commercial quantum computers include Finland's first commercial 50-qubit quantum computer with VTT, IQM-led consortium's (Q-Exa) HPC quantum accelerator in Germany, and IQM processors will also be used in the first quantum accelerator in Spain. IQM has over 280 employees with offices in Paris, Madrid, Munich, Singapore, and Espoo.

http://www.meetiqm.com

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Theoretical framework

We develop QNIR theory starting from general RC theory. RC is a computational paradigm and class of machine learning algorithms that derives from RNNs. RC involves mapping input signals, or time series sequences, into higher dimensional feature spaces provided by the dynamics of a non-linear system with fixed coupling constants, called a reservoir. Having a smaller number of trainable weights confined to a single output layer is a core benefit of RC because it makes training fast and efficient compared to RNNs. RC has a number of properties that should be met28,29 including adequate reservoir dimensionality, nonlinearity, fading memory/echo state property (ESP) and response separability.

For the univariate case, a reservoir, f, is a recurrent function of an input sequence, (u_t), and prior reservoir states, (bar{x}_{t-1}), as

$$begin{aligned} bar{x}_t = f(bar{x}_{t-1},u_t). end{aligned}$$

(1)

As output sequences, (bar{x}_t), training sequences are selected between time-steps (t=t_i) and (t=t_f), and form a training design matrix, (textbf{X}_{tr}). The initial sequence, (t

$$begin{aligned} textbf{y} = W^T textbf{X}_{tr}, end{aligned}$$

(2)

is trained based on least squares, where (textbf{y}) is the target vector and W is an initial weight vector. The trained model has the form:

$$begin{aligned} hat{textbf{y}} = W^T_{opt}textbf{X}, end{aligned}$$

(3)

with an optimized weight vector, (W^T_{opt}), to give a predicted sequence, (hat{textbf{y}}), from new sequences, (textbf{X}).

Circuit channel diagrams of the QNIR computer in the unrolled view, composed using30. The initial state of the quantum reservoir is (|+rangle ^{otimes n}) and the quantum channels labeled (mathscr {T}_{u_i}) evolve the density operator as in Eq. (4), where N quantum circuits are required for N time steps. A number of output sequences, n, are concatenated from sequential, single-qubit expectation value measurements (langle Z_{i} rangle) on n qubits.

For QNIR with artificial noise channels, the RC framework that has been developed is now instantiated in the following way. The density operator evolves in time steps as

$$begin{aligned} rho _t = mathscr {T}_{u_t}(rho _{t-1}), end{aligned}$$

(4)

where the reservoir map (mathscr {T}_{u_t}) is composed of a sequence unitary quantum gates, (U_i), and associated artificial noise channels, (mathscr {E}_i), that are completely positive and trace preserving (CPTP). The reservoir map can be represented as a composition of quantum channels

$$begin{aligned} mathscr {T}_{u_t}(rho _{t-1}) = mathscr {E}_{U_K} circ ldots circ mathscr {E}_{U_2} circ mathscr {E}_{U_1} (rho _{t-1}), end{aligned}$$

(5)

where the notation (mathscr {E}_{U_i} = mathscr {E}_i( U_i rho U_i^{dagger } )) is used for clarity and to emphasize that each quantum gate is acted on by a noisy channel and K is the number of noise channels in the time step. We will refer to (mathscr {T}_{u_t}) as a noisy quantum circuit. QNIR requires an initial washout phase, (t

The unitary, noiseless part of the quantum circuit is composed of an initial layer of RX gates followed by an entanglement scheme of ({RZ!Z}_{i,j}) gates, which are 2-qubit entangling gates

$$begin{aligned} (C!X_{i,j}RZ_j(theta )C!X_{i,j})RX^{otimes n}(theta ) = {RZ!Z}_{i,j}(theta )RX^{otimes n}(theta ), end{aligned}$$

(6)

where all (RX(theta )) and (RZ(theta )) rotation gates encode the time series data with a scaling map, (theta =phi (u)). The purpose and structure of the unitary encoding gates is detailed in subsection: Reservoir circuit designs.

Single-qubit expectation values, (langle Z_{i} rangle = Tr(Z_i rho )), are measured for all n qubits at each time-step,

$$begin{aligned} h_t = [langle Z_{1} rangle ,langle Z_{2} rangle ,ldots ,langle Z_{n} rangle ]^T, end{aligned}$$

(7)

as shown in a circuit diagram in Fig. 1. Figure2 depicts that time series values are encoded to all reservoir qubits and (langle Z_{i} rangle) are measured of all qubits, which are concatenated for each time step to give n reservoir feature sequences (q_i = {langle Z_{i} rangle }_{t=0}^N), where N is the number of time steps. In turn, (q_i) form a design matrix (textbf{X}) and the QNIR model is trained as in Eq. (3). A schematic of the full QNIR computer is shown in Fig. 3.

This drawing represents many repeats of data encoding of a single value, (u_i), to all reservoir qubits (left) and measurements of single-qubit Z expectation values (right). This two-part process occurs at each time step i to build feature signals by concatenation. Noisy quantum circuits are shown for each time step in Fig. 1. This drawing shows an example of a four-qubit reservoir with fixed, pair-separable dynamics.

In this graphic the first layer contains an array of duplicates of a single time series value. Each value in the input array is encoded to all qubits of the reservoir as in Eq. (6). The second layer is a quantum reservoir with arbitrary entanglement scheme, represented by connecting lines between qubit nodes. The Z observable expectation value, (langle Z_{i}rangle), is measured for all qubits. These measurements are repeated and concatenated to build output signals, (q_i). In the final layer, these signals are used in multiple linear regression for time series prediction, as in Eq. (3).

It is important in RC and by extension QRC that the reservoir system can capture the temporal dynamics of the target system. To ensure this we implement a reservoir optimization scheme for QNIR. The artificial noise channels, (mathscr {E}_i), of the quantum reservoir circuit are iteratively updated by an optimization routine with an MSE cost function based on the time series prediction performance. This serves to optimize the quantum reservoir for time series prediction. Details of the optimization approach are in subsection: Reservoir noise parameterization.

This section is concerned with the architecture and purpose of the unitary gates of the quantum circuit, the high-level structure of the noisy quantum circuits and entanglement scheme. The details of the noise scheme are covered in subsection: Reservoir noise parameterization.

The initial state of the quantum reservoir, (|+rangle ^{otimes n}), is prepared by an initial Hadamard gate layer. Continuing with Eq. (6), an n-qubit QNIR circuit has a fixed sequence of quantum gates

$$begin{aligned} begin{aligned} U_{b}(u)&= (C!X_{i,j}RZ_j(theta )C!X_{i,j})RX^{otimes n}(theta ) \&= {RZ!Z}_{i,j}(theta )RX^{otimes n}(theta ) end{aligned} end{aligned}$$

(8)

where i,j are indices for two qubits that denote the placement of multiple 2-qubit (RZ!Z) entangling gates. The decomposed form of the circuit with (C!X) and RZ gates23 is implemented with noise channels (see subsection: Reservoir noise parameterization). A time series data value, u, is encoded to all (RX(theta )) and (RZ!Z(theta )) gates by angle (theta = phi (u)), where (phi) is a scaling map.

To implement the recurrent architecture of QNIR, a set of N quantum circuits are executed for a time series ({u_t}^N_{t=0}). The first circuit encodes ({u_0}), the second circuit encodes ({u_0,u_1}), and the Nth circuit encodes ({u_t}^N_{t=0}) as

$$begin{aligned} text {U}_{t=N} = U_{b}(u_N) ldots U_{b}(u_1)U_{b}(u_0). end{aligned}$$

(9)

All unitaries (text {U}_t) for arbitrary t constrain the i expectation values to a zero bitstring

$$begin{aligned} langle Z_{i} rangle _{t} = langle Phi _0|text {U}^{dagger }_t Z_i text {U}_t |Phi _0rangle = 000..., end{aligned}$$

(10)

where (|Phi _0rangle = |+rangle ^{otimes n}) is the initial reservoir state and (Z_i) represents n single-qubit Z measurement operators. It is the action noise that ensures the qubit signals are non-zero, feature sequences, (q_i). Now considering the full QNIR circuits with artificial noise, the noisy quantum circuit for the final iteration, encoding ({u_t}^N_{t=0}), is the quantum channel

$$begin{aligned} {varvec{mathscr {T}}}_{N} = {mathscr {T}}_{u_N} {circ } ldots {circ } {mathscr {T}}_{{u}_{2}} {circ } {mathscr {T}}_{{u}_{1}}. end{aligned}$$

(11)

The noisy quantum circuit with artificial noise scheme will be detailed in the next subsection: Reservoir noise parameterization. This scheme may further reduce resources by circuit truncation based on a memory criterion29,31,32,33.

For (RZ!Z_{i,j}) gates, the degree of entanglement between qubits i and j is a function of (u_t). It is important that the range of magnitudes of the data values is constrained and we observe that values much larger than (2pi) cause undesirable effects. We consider benchmarks that do not require re-scaling.

Drawing from the close connection with quantum feature maps23,34,35,36, entanglement schemes are defined by the number and placement, i.e. the architecture, of (RZ!Z) gates in Eq. (6). Common entanglement schemes that could be trialed are full, linear, pair-wise, and what we call pair-separable used inSuzuki et al.11. The pair-separable (PS) and linear entanglement (LE) schemes explored in this work have (RZ!Z) gates indexed as (i,j in {(0,1),(2,3),(4,5),...,(N-1,N)}) and respectively (i,j in {(0,1),(1,2),(2,3),...,(N-1,N)}). To clarify, for an LE scheme, every additional (RZ!Z) gate is in a new circuit layer, increasing the circuit depth each time. The LE scheme creates whole circuit entangled states23. The state vector for a PS entanglement scheme evolves in a product state of qubit pairs, (|psi rangle = bigotimes _{i=1}^{n/2} |phi rangle _i), where (|phi rangle _i) are two-qubit entangled states. The state, (|psi rangle), can be efficiently classically simulated and can be parallelized in classical simulation or on quantum computers37,38.

QNIR uses noise as a necessary resource to generate non-trivial feature sequences. We use artificial noise that can be programmed to a quantum computer. Within this scheme, many such artificial noise models can be implemented to produce different effects. To implement a noise scheme, we associate parameterized, single-qubit noise channels with each unitary gate in the quantum circuit, Eq. (6), as shown in Fig. 4. Note that this differs from Kubota et al.12, where noise channels were situated at the end of every time step. In the following, we assume each noise channel depends on a single noise parameter.

A 2-qubit quantum circuit channel diagram of an reservoir noise parameterization. Each unitary gate has an associated noise channel represented by (mathscr {E}(p_i)). This represents the novel quantum circuit parameterization approach proposed in this work.

This graphic shows the QNIR noise optimization scheme. The quantum model is trained and tested iteratively in a classical optimization loop, where dual annealing or evolutionary optimization are used. The quantum reservoir circuits have a number of gate-associated noise channels, each of which has a single error probability parameter that is iteratively updated.

Noise channels are associated with all quantum gates in the reservoir circuit in Fig. 4. Each noise channel (mathscr {E}(p)) is a function of a probability for the noise effect to occur. We use probabilities, (p_i), to parameterize the reservoir for optimization. The number of probability parameters scales linearly with the number of qubits. For pair-separable entanglement reservoir, the number of parameters is (n_{p_i} = frac{7}{2} n), where (n=2,4,6,...), and for linear entangled reservoir (n_{p_i} = 6n-5), where (n=2,3,4,...).

QNIR resource-noise optimization is performed through iterative training (Eq. 2) and testing (Eq. 3) of QNIR, giving optimized noise probability parameters, (p_i in textbf{p}) (see Fig. 5). The parameters in the initial parameter vector, (textbf{p}), are probabilities randomly selected from a uniform distribution, (p_i sim U(0,1), forall i).

Two optimization approaches were trialed in this work, evolutionary optimization27 and dual annealing39, where the latter is available in the SciPy optimization package40. The mean squared error (MSE) was used as a suitable cost function to measure prediction performance, which is minimized as

$$begin{aligned} min _{textbf{p}}; { text {MSE}(hat{textbf{y}}(textbf{p}),textbf{y}) : p_i in [0,1], forall i }, end{aligned}$$

(12)

where (hat{textbf{y}} = W^T_{opt} textbf{X}(textbf{p})) is the QNIR test set prediction and (textbf{X}(textbf{p})) are the reservoir signals matrix dependent on noise probabilities (textbf{p}).

In this work, we use only reset noise channels that can be simply implemented with a classical ancilla system (see next subsection: Reset noise).

We propose a simple hybrid quantum-classical algorithm for a reset noise channel that consists of probabilistically triggering a reset instruction using a classical ancillary system. A deterministic reset instruction is an important element of a quantum instruction set, for the need to reset qubit states. A quantum instruction set is an abstract quantum computer model41,42. In this work we consider a reset to (|0rangle) noise channel given by (mathscr {E}_{PR}(rho ) = p|0rangle langle 0| + (1-p)rho), where p is the reset probability43. (mathscr {E}_{PR}(rho )) is trace-preserving, (Tr(mathscr {E}_{PR}(rho ))=1).

Using dynamic circuits, quantum computers can implement a reset instruction with a mid-circuit measurement followed by a classically controlled quantum X gate that depends on the measurement outcome44 (see Fig. 6). For example, this is how a reset is now implemented on IBM quantum computers supported by OpenQASM341.

A deterministic RESET instruction (left) is executed with this dynamic circuit. This can be used as a basis for a reset noise channel, (mathscr {E}_{PR}). A single line represents a qubit and a double-line represents a classical bit. A model classical ancillary system (right) would be executed on a classical computer. The classical NOT gate, (X_p), is executed with probability p, which in turn triggers a classical controlled RESET instruction with probability p.

In classical computing, execution of a probabilistic instruction is triggered using a random number generator (RNG), such as those widely available in software as PRNGs or in hardware as HRNGs. Here we employ a classical RNG to probabilistically activate a reset, which is identical to reset noise. In this way, artificial reset noise is implemented without ancilla qubits. Ancilla qubits would be an undesirable overhead in the larger scheme presented in this work in which unitary gates require potentially many corresponding noise channels. This hybrid approach may be viable for other noise channels. For example, reset noise can approximate amplitude damping noise to high precision43.

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IonQ (IONQ -1.58%) has taken investors on a wild ride since its public debut. The quantum computing company merged with a special purpose acquisition company (SPAC) and started trading at $10.60 per share on Oct. 1, 2021. IonQ's stock then nearly tripled to its all-time high of $31 on Nov. 17, 2021, but plummeted over the next 13 months to a low of just $3.04 per share on Dec. 28, 2022.

Like many other hyper-growth stocks, IonQ lost its luster as rising interest rates popped its bubbly valuations, highlighted its losses, and drove investors toward more conservative investments. But after bottoming out, IonQ's stock bounced back to about $12 again. Let's see if this volatile stock can stay ahead of the market over the next 12 months.

Image source: Getty Images.

Traditional computers use binary "bits" of zeros and ones to process data. Meanwhile, quantum computers use "qubits" which can store zeros and ones to simultaneously process data at much faster rates. That sounds like a generational leap forward in computing technology, but quantum computers are still much larger than traditional computers.

For example, IBM'scasing for a single qubit processing unit (QPU) is about six feet wide. Alphabet's Google has been developing a qubit processing system that is about 20 feet wide.

IonQ is tackling that problem with a newer type of QPU system that is only two inches wide. It built that system with a "trapped ion" architecture, which makes it smaller and easier to scale. That technology enabled it to build the "world's most powerful trapped-ion quantum computer," and it serves up that computing power as a cloud-based service through Amazon'squantum cloud computing service Braket, Microsoft'sAzure, and Google Cloud.

IonQ measures its quantum processing power in algorithmic qubits (AQs). During its pre-merger presentation, it claimed it could grow from AQ 22 in 2021 to AQ 29 in 2023. However, it actually hit AQ 29 seven months ahead of schedule in the first quarter of 2023 -- and it's now set on reaching its next milestones of AQ 35 in 2024 and AQ 64 in 2025. After that, it expects to achieve exponential growth and achieve AQ 1,024 by 2028.

IonQ initially predicted its revenue would reach $5 million in 2021, triple to $15 million in revenue in 2022, and then reach $34 million in 2023 as more companies used its services. But in reality, it only generated $3 million in revenue in 2022 and $11 million in revenue in 2023. It expects its revenue to grow about 70% to 73% to about $19 million in 2024.

IonQ's failure to meet its pre-merger targets caused many investors to lump it together with other SPAC-backed companies that overpromised and underdelivered. Its red ink made it even less appealing: It narrowed its net loss from $106 million in 2021 to $49 million in 2022, but racked up an even wider loss of $71 million in the first half of 2023.

IonQ's broken promises caused its stock to sink to its all-time low last December, but the growing interest in artificial intelligence (AI) stocks over the past year drove the bulls back to its stock. The continued expansion of the AI market will likely drive the growth of the quantum computing market as companies explore even faster ways to process data.

IonQ still has room to grow. IDC expects the quantum computing market to grow at a compound annual growth rate (CAGR) of 48% from 2022 to 2027, and IonQ could pace with the market if it continues to increase its computing power.

With an enterprise value of $2.18 billion, IonQ might seem overpriced at 115 times this year's sales. However, analysts expect its revenue to rise from $19 million in 2023 to $88 million in 2025, which would represent a CAGR of 115%.

If you think IonQ can successfully scale up its business and hit those targets, then its stock might not seem too expensive at 25 times its 2025 sales. It could also become a compelling takeover target for a larger tech company if it proves its trapped-ion technology is superior to other quantum computing technologies. However, the recent departure of its co-founder and chief science officer Chris Monroe -- who co-developed the trapped-ion process -- raises a few red flags.

I believe IonQ's stock will remain volatile as high interest rates and other macro headwinds generate headwinds for unprofitable hyper-growth stocks. It might still be a good speculative play for investors who can afford to tune out the noise for a couple more years, but I'm not too confident it can outperform the market over the next 12 months.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. Leo Sun has positions in Alphabet and Amazon. The Motley Fool has positions in and recommends Alphabet, Amazon, and Microsoft. The Motley Fool recommends International Business Machines. The Motley Fool has a disclosure policy.

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The Science Minister George Freeman will today (Thursday 2 November) outline the recipients of over 14 million in funding, aimed at benefiting the UKs thriving quantum sector.

At the UK National Quantum Technologies Showcase, taking place in London today, Minister Freeman will set out how the government is continuing with its vision to become a quantum-enabled economy by 2023.

The showcase will present the latest in ground-breaking applications of the technology which will potentially revolutionise many aspects of life in the UK. Quantum technologies could bring enormous benefits to the economy, such as making it possible to solve complex problems impossible to solve with even the most powerful high-performance classical computers, and opening entirely new frontiers in sensing, timing, imaging, and communications.

The event, which last year welcomed over 1,000 delegates, with 67 exhibitors from 34 different countries, is organised by National Quantum Technologies Programme, which was established in 2014 and backed by 1 billion of government funding.

The National Quantum Strategy, published in March 2023, commits a further 2.5 billion to developing quantum technologies in the UK over the 10 years from 2024 which will aim to generate at least an additional 1 billion of private investment into the programme.

The announcements made today will include:

George Freeman MP, Minister of State at the Department for Science, Innovation and Technology said:

We have identified Quantum as one of the 5 transformational technologies in which the UK is a global leader, which is why we have set out a 2.5 billion 10 year industrial strategy to support the quantum sector here in the UK. Quantum is set to transform computing, imaging and sensing, cyber security and position, navigation and timing with major industrial applications from drug discovery to defence, fintech, and much more. That is why I am here today at the Quantum Showcase to announce this package of UK funding and programmes.

Our partnership between the National Quantum Computing Centre and IBM will provide cloud quantum computing access for companies, agencies, national labs and other organisations to help boost UK quantum computing infrastructure. Our 30 million quantum testbed programme will build the worlds first quantum computing testbed to assess and benchmark machines. Our funding for collaborative programmes with the Netherlands and Canada is a sign of our commitment to develop global quantum standards and networks.

This is an exciting day for the UK quantum sector.

Quantum technologies one of the governments 5 critical technologies are devices and systems using quantum mechanics to provide capabilities that classical machines like binary computers cannot.

The technology already offers possible solutions to some of our greatest challenges in society and provide future capabilities that are yet to be explored. These technologies hold the potential to tackle intricate problems that currently surpass the capacities of even the most advanced classical computers and will allow us to reach new frontiers in sensing, timing, imaging, and communications. Over the next 10 years, quantum technologies are expected to revolutionise many aspects of life in the UK and bring enormous benefits such as helping to grow our economy and create well-paid jobs across the country one of the Prime Ministers 5 priorities.

The National Quantum Strategy sets out a bold and ambitious approach to supporting quantum technologies in the UK across the broad spectrum of quantum computing, sensing, timing, imaging and communications. It shows how the UK will develop its strengths across different hardware platforms, software and components, and reinforce our capabilities throughout the supply chains.

Just last month, Secretary of State Michelle Donelan opened PsiQuantums new state-of-the-art research and development (R&D) facility at Sci-Tech Daresbury, which is supported with 9 million from the government. In June, Minister Freeman announced 45 million in funding to support universities and businesses working in the UKs quantum technologies sector.

As part of the Innovate UK led Small Business Research Initiative (SBRI) competition, 6 projects have been awarded 10.6 million to accelerate the development of components and systems for quantum network technologies.

These technologies will transform the way in which we distribute, secure, and process our information to meet the challenges of a communication network that is growing in capacity and complexity as our economy becomes increasingly underpinned by data. The funding will assist in the delivery of deployable prototypes into the hands of customers, building the UKs leadership in this emerging global market.

The projects chosen to receive funding include delivering modular systems for connecting quantum processors into networks at scale and developing high-bandwidth quantum-secure communications between satellite and ground networks, they are set to conclude in 2025.

The National Physical Laboratory (NPL), in collaboration with government and industry partners, will launch a UK Quantum Standards Network Pilot. This pilot network builds on the commitments made in the National Quantum Strategy which recognises the importance of technical standards to support the global commercialisation of quantum technology. The pilot network will ensure the UK is at the forefront of establishing global standards for quantum. It will provide a focal point on standards for UK industry and develop initial plans for industry outreach, standard development road mapping and international engagement, helping overcome barriers to the realisation of the potential of quantum technologies.

The aim of the pilot network is to evolve into a centre that coordinates the UKs engagement with global standards, ensuring that the UK continues to be at the forefront of the quantum revolution. NPL will collaborate with the British Standards Institution (BSI), the Department for Science, Innovation and Technology (DSIT), UKQuantum, the National Cyber Security Centre (NCSC) and the National Quantum Computing Centre (NQCC) on the pilot network.

The UK has signed two new quantum agreements with Australia and the Netherlands to help harness the constant creation of new knowledge, understanding and insights from our innovation ecosystems. International partnerships will play a crucial role in delivering the UKs ambitions for quantum technologies as set out within the National Quantum Strategy. The UK has already signed agreements with the US and Canada which set out areas for closer cooperation covering research and development, commercialisation, investment and skills.

Australia is a key partner and agreeing to closer working on quantum will also build on opportunities presented through the Free Trade Agreement and existing science and technology links, such as the Cyber and Critical Technology Partnership.

The Netherlands have a strong history and culture of technology and the agreement will see a deepening of the collaboration on science and innovation between the 2 countries. It will also support efforts in both countries to develop ethical and governance principles for the responsible use of quantum technology, for the benefit of society as a whole.

Collaboration between key international partners will be essential to build mutual capabilities and to grow industrial opportunities within quantum technologies. Alongside the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP), Innovate UK is investing 4.2 million in 11 projects to strengthen collaborative research and development through Canada-UK partnerships.

This funding will help to develop real-world quantum technologies for commercial use in networking, sensing, and scalable solutions to quantum computing, alongside developing the supply chain.

The NQCC will provide assured quantum computing access to UK-based organisations to drive the research and development work that may benefit from the technology. This reflects the NQCCs vision to enable the UK to solve some of the most complex and challenging problems facing society by harnessing the potential of quantum computing. To deliver this, the NQCC will have multiple quantum service providers to enable the growth of the UKs quantum computing user community, with access to a wide range of state-of-the-art quantum machines.

The objective is to drive new research horizons and serve the UK research community to enable proof-of-concept projects, feasibility studies and discovery-led science. As a part of the initiative, the NQCC will engage with IBM Corporation to provide UK-based quantum computing users priority access to the full fleet of IBMs quantum machines. Aligned with the recently published National Quantum Strategy, and the commitment of 2.5 billion of investment, the NQCC remains committed to working with organisations across government, industry and the research community, to support the delivery of assured quantum computing capabilities for the UK and build the user community for quantum computing.

The centre seeks to enable the UK to become a quantum-ready nation and take full advantage of the benefits that quantum computing can offer, by supporting the UK-based organisations.

The NQCCs first facility, the NQCC Innovation Hub, is now ready to start hosting the development and operation of quantum computing testbeds.

The NQCC is investing 30 million in projects that will deliver a series of quantum computing testbeds, based on different hardware architectures by March 2025. The prototypes that come from the competition, which is being delivered by Innovate UK, will accelerate the development of scalable quantum computers by enabling detailed characterisation and benchmarking of early-stage machines.

In establishing these quantum testbeds, the NQCC is aiming to fill a gap between lab-based experimentation in academia and the growing number of commercial quantum computers that have been built around proprietary technologies. The availability of testbeds will provide an experimental framework for ongoing efforts to develop methodologies for testing, and validating, the performance of quantum computers, which so far have largely relied on theoretical approaches. The initial response to the expression-of-interest call has shown that developers can see the value in opening up their platforms within the protected environment offered by the NQCC. The competition results will be announced in early 2024.

Small Business Research Initiative competition - Quantum Networks, Enabling Components & Systems list of projects:

Canada - UK Commercialising Quantum Technology Programme list of projects:

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New technologies on show at Quantum Showcase as Science ... - GOV.UK

In this episode, we explore the synergy of quantum computing and drug discovery with the chief scientific officer and co-founder of Qubit Pharmaceuticals, Jean-Philip Piquemal.

Qubit Pharmaceuticals was founded in 2020 with the vision of co-developing, with pharmaceutical and biotech companies, new, more effective, and safer drugs. The company uses its Atlas platform to discover new drugs through simulation and molecular modeling.

The company uses a supercomputer, Gaia, created in collaboration with artificial intelligence provider Nvidia.

Piquemal said the company is a digital pharma, meaning it doesnt have wet labs and instead uses computers to predict new drugs. By doing this, Piquemal said the company hopes to discover new drugs for various diseases more quickly.

Piquemal said Atlas is the companys computational platform that is capable of doing many different types of computation.

We dont have a lab where people are doing some synthesis, but they are performing computation on their screens, Piquemal said.

They can visualize the molecules. They can modify the molecules. And they can compute a lot of properties towards the idea that we will see in the computer the drug interacting with its target. So, its like a Swiss knife with hundreds of different methods and very strong computational capabilities to be able to handle the largest supercomputers.

Piquemal said the dilemma for pharma companies is that, while they find a lot of drugs, there may be difficulties synthesizing them, or the cost may be too high. They may show toxicity, or have side effects, but rarely is this known until the end of the process.

We have a lot of candidates. But with the idea that probably 99.9% of initial ideas will be already validated as failures. So thats exactly what we want to do. We want to fail as much as possible in the computer in order to not fail when we come to the real world.

This computational revolution will really unleash the power of pharma to help a lot more people.

A digital twin, according to Piquemal, is the idea that you will have a computational model that will be as good as the real object.

So, in pharma, usually what we are trying to digitalize as a twin is the target of the drug. And the target of a drug in pharma is a protein. A protein has a 3D structure that you can get from experiments. You need to find a drug and to find its location into this target, Piquemal explained.

When you look at that in terms of surface, its a little bit like the surface of the Moon or the surface of a planet like Mars. There are a lot of different locations. And so the digital twins purpose is to reproduce that in the computer. You can explore the surface of this target to find the best location for the drug. Thats a tough problem, and thats why you need a lot of computation to do this.

The company recently partnered with Pasqal, a quantum computing company, and their project was chosen as one of 12 alongside NASA and Harvard University for the Quantum for Bio program, an offshoot of the Wellcome Trust, which aims to accelerate the use of quantum computing in drug discovery and healthcare by developing applications that will benefit from the arrival of quantum computers within three to five years.

Pasqal and Qubit Pharmaceuticals are the only French consortium to be awarded a prize in this global call for projects. Together with the Unitary Fund, they will receive $4.5 million of the total $40 million awarded.

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Is quantum computing the future of drug discovery? - Labiotech.eu