What is Scotland USP in quantum computing and how can we promote it?

Quantum Computing is an internationally highly competitive game, with a range of government and companies investing and a technology race between competitive technical solutions.
Articulating Scotland unique strengths in this landscape, understand how to leverage them and how they will lead to capture of market shares is essential.

What applications using which algorithmic techniques do you believe have the greatest promise for nearer-term implementation in the field of Operational Healthcare?

We are researching possible quantum computing applications to healthcare (and finance). The theatre list patient allocation problem is an example problem that we have been investigating and we would welcome an alternative perspective on. How would you apply quantum computing techniques to this problem? We are also concerned with the challenge of barren plateaus, as this appears to be a key challenge for a wide variety of quantum algorithms, and would like to address emerging techniques you think have the best chances of overcoming this challenge.

What standardized technologies (hardware, software, and process) will be needed to realize effective classical-quantum hybrid computation?

Qasm represents a possible candidate in the middle of the stack, but what standards are likely to emerge above and below? For example, orchestration languages for quantum enhanced ML, standardized microwave componentry for qubit control, the same for measurement technology, etc. Will there be new development methodologies to accelerate the quantum application identification and development cycle, which seems rather long-winded today, cf: Quantum software lab workshop | Nqccslab (ediparlab.wixsite.com).

If you had access to a machine that had 100 qubits and could process circuits with and equivalent circuit depth of 100, how could you help non-quantum specialists with interesting domain problems solve industrially relevant usecases?

We would like to present some recent advances in technology, such as circuit knitting and dynamic circuits, and help prepare academics for the upcoming possibility to take part in the 100×100 challenge. Ideally we would have a mix of academics and industry from application domains (such as biologists, economists, manufacturing…) together with academics that are interested in applying their algorithm expertise and lateral thinking skills.

https://arxiv.org/abs/2209.06841

Can quantum computing be applied to enhance the chemical analysis of bio-nutrients?

The nature inspired robot-iDigest is supporting food businesses to reduce operational cost while enhancing their sustainability profile by ensuring that inedible food wastes are conveniently, processed onsite to bio-nutrients. iDigest uses enzymatic bio-catalysis, a process that can be optimised by understanding the chemical composition of the food waste as well as monitoring the metabolites produced, thereby, improving the use of the recovered bio-nutrients from iDigest. We are currently using Principal component analysis and neural network for data analysis and predictive modelling.

More details about the iDigest can be found here-https://intellidigest.com/products/idigest/.

When it is possible to use a 10,000 Qubit for Fusion applications?

Interested in use of Quantum Computing for Fusion. 10,000 Qubits are of importance since I anticipate that some QC will be able to show off 10,000 capabilities within 4–7 years. The interest in fusion applications is primarily on stellarator Field Confinement.

What challenges and opportunities might an international payments company face when working with Quantum Encryption within their payment flow communication protocols?

Paysafe is an international payments company which works in many vertices with various levels of risk , it may wish to make use of quantum encryption to secure its payment protocols but needs a strategy for this and would consider the following: Are challenges likely to be experienced in the following areas?

– Would re-architecture be needed to accommodate a step up or step down from traditional encryption and if so is this even advisable ?
– Is there likely to be a two tired payment network ,one network / system using traditional encryption and the second being purely quantum?
– What would the impacts be on current legalisation?
– Are there likely to be challenges in encrypting the specific protocols due to the way they are formed or ‘packaged’.
– What happens if the payment flow needs to be sent to other financial institutions who are not quantum ready?

How would it be possible to build a dynamic resourcing model which predicted the number of calls, types of incidents, deployment times, missing persons, updated in real time to give us the best possible idea of likely response capacity?

We don’t currently have any in-house models that could be sped up by a quantum computer or HPC but want to begin the conversation and have a discussion about our data and operational problems. Rough number of calls per year is 3m, about 50% of which become incidents, just under half of which result in a deployment. The two problems we’d like to focus on are the accuracy of our forecasting and the matching of resources to demand.

Our command and control centres are run on a shift basis with a professional judgement understanding of which days/shifts are busiest, which is a rather crude method, looking at volume of calls and not being able to quantify the impact of different incident types etc. In terms of officer deployment, this exists within a complicated mesh of different factors. There is an overarching strategic workforce plan, which determines how many officers we require in different parts of the country, and more localised decisions around shifts and precise deployment/tasking. On top of this there are specialisms (such as firearms) that certain officers have that can be called upon, and a repository of officers that can be moved around the country to deal with particular surges in demand.

Given the current state of QC algorithm development what classes of mathematical models are amenable to being solved in the NISQ era and can quantum machine learning , specifically quantum generative models ,enhance combinatorial optimisation.

Quantum Base Alpha, is a start-up based at the Institute of Physics Accelerator in King’s Cross London. We have been researching using Quantum Computing for real world applications particularly those fighting the existential crisis of Climate Change.
Our first project is to investigate optimisation aviation emissions for which we have recently been in receipt of an Innovate UK award. This project has been done in collaboration with the EPCC and we are actively seeking out further academic partners to extend and deepen our work.

Who is going to win the quantum computing race?

We are interested in the potential applications of a quantum computing in the design of new materials (e.g. semiconductor materials, display materials, battery materials, etc). The hardware platforms we are currently interested in are the major ones like superconducting, ion trapped and Si qubits, but we want to learn more about hybrid superconducting-semiconductor qubit technology.

How can we address barriers to adoption, ecosystem cooperation, and government support for quantum Computing?

Finding end-users and early adopters of quantum computing is a challenge, many industries do not have the understanding or expertise to identify how and when quantum applications may benefit their business. Equally, many industries are unaware of the government interventions for supporting innovation in this field. This question shall explore a) which industries are likely to benefit from quantum computing earliest b) how to identify a problem which is ripe for ‘quantum’ approaches c) how government can support R&D efforts in quantum applications and d) from a local perspective, strengths of the Scottish ecosystem which could be an early ‘testbed’ for quantum applications. Academics who are interested in supporting collaboration with end-users and early adopters, whilst building a symbiotic relationship with government, are encouraged to join this workshop. We actively support academics in wider non-technological fields (i.e. economics, business, or social sciences) to join this discussion. Further reading: https://www.ifm.eng.cam.ac.uk/uploads/Research/BMI/2022-11-11-QuantumWhitePaper.pdf.

Can quantum random number generators (QRNGs) help to solve combinatorial optimization problems with greater accuracy?

Many combinatorial optimization (CO) problems in chemistry, finance, etc., can be often solved efficiently with randomized algorithms such as Monte Carlo (MC) simulations. Markov-chain calculations are used in molecular MC simulations. At the heart of all these randomized algorithms lies the generation of trial changes based on random numbers and a decision based on the comparison of a calculated property with a random number. So, intuitively it seems that a set of fundamentally (truly) random numbers may help in improving the accuracy of the optimized result. This inspires us to deliberate and gain insights on if and how the use of QRNGs can impact the accuracy obtained while solving such CO problems.

What are the challenges in scaling a distributed network of quantum computers for overcoming the current limitations of the number of qubits available on a single machine?

With rapid progress in quantum technologies, worldwide research towards enabling a global quantum internet that can help to efficiently route and retrieve quantum and classical information is gaining importance. Distributing entanglement via the use of quantum repeaters across long distances and preserving entanglement fidelity over such distance scales via the use of distillation techniques is being envisaged through novel proof-of-concept demonstrations. While there are quite a few candidates that can potentially be used to build the qubits of a quantum computer including trapped ions, neutral atoms, superconducting loops, photonics, etc., each of them possesses certain advantages and drawbacks in terms of developing large-scale quantum computers that can deterministically outperform the classical counterparts. In this light, we are curious to understand what are the current limitations that prohibit connecting these small-scale quantum computers, built out of different technologies, as the nodes in a quantum network and approaching larger-scale distributed quantum computing.

How can one estimate the number of logical qubits and coherence times required in a NISQ computer such that a hybrid (classical + NISQ) algorithm will outperform an algorithm running entirely on classical hardware available:
i) on the world’s top ranked supercomputer?
ii) at the same cost as the hybrid (classical + NISQ) setup?

Based on the current evolution on Quantum Computing hardware technology, it seems that NISQ hardware will be a reality for quite some time to come. This means limited number of logical qubits and limited coherence time. This further implies that it will be the hybrid quantum algorithms that perform part of the computing on NISQ and part of the computing is performed on classical hardware. We can see that hardware vendors are announcing NISQ devices with a larger number of qubits in every new product. So this raises the question of whether the use of NISQ devices will ever be useful.
Even in the use of NISQ hardware one has to account the noisy behaviors and the fact that applying gates to control qubits and taking measurements will take non negligible time. Plus we also have to account for the number of shots/samples to be executed on the NISQ hardware. For large workloads like optimization and Quantum Chemistry it is essential to establish the number of qubits, the device’s reliability and coherence times – that will enable a hybrid NISQ setup to achieve even a modest 2X speed up over an end to end classical computing setup.

Can quantum computers demonstrate practical computational advantage with less than 1 million physical qubits?

So far, rigorous proofs of quantum advantage or speedup have only been carried out for very few quantum computing algorithms, including Shor’s, Grover’s and Gaussian Boson Sampling. Useful applications of Shor’s and Grover’s applications are well known, but will require hundreds of thousands or millions of qubits to be implemented using today’s hardware platforms. Will hardware improve enough to run these with one-tenth or one-hundredth as many qubits?
Alternatively, are there other algorithms or approaches, (e.g. QML) that might demonstrate practical speedups with far less qubits required?

Is it possible to resolve the number of photons in an optical pulse at telecom wavelengths, using only room-temperature equipment?

Single photon, or photon-number-resolving (PNR) detection at wavelengths between 1300-1600 nm has been demonstrated using SNSPDs (silicon nanowires) and tungsten TES detectors. Both require cryogenic cooling. Are there any other potential technologies, in earlier stages of development, that could potentially achieve similar performance while operating at room temperature?

How can we address barriers to adoption, ecosystem cooperation, and government support for quantum Computing?

Finding end-users and early adopters of quantum computing is a challenge, many industries do not have the understanding or expertise to identify how and when quantum applications may benefit their business. Equally, many industries are unaware of the government interventions for supporting innovation in this field. This question shall explore a) which industries are likely to benefit from quantum computing earliest b) how to identify a problem which is ripe for ‘quantum’ approaches c) how government can support R&D efforts in quantum applications and d) from a local perspective, strengths of the Scottish ecosystem which could be an early ‘testbed’ for quantum applications. Academics who are interested in supporting collaboration with end-users and early adopters, whilst building a symbiotic relationship with government, are encouraged to join this workshop. We actively support academics in wider non-technological fields (i.e. economics, business, or social sciences) to join this discussion. Further reading: https://www.ifm.eng.cam.ac.uk/uploads/Research/BMI/2022-11-11-QuantumWhitePaper.pdf.