Our various activities are targeting scientific knowledge creation in several complementary domains (Foundation of Quantum Information, Programming and Modeling, Software Development, Algorithm and Protocol Design, Complexity Analysis, Error Correction-Mitigation, Verification, Benchmarking and etc). Furthermore, through our unique use-case driven program we combine these research discoveries matching them to specific applications from several sectors such as Pharmaceutical and Materials, Finance, Logistics, Cyber Security. In this way our research acts as filters through the pathfinding case studies experiment towards discovering the true (if any at all) quantum enhancement possible for challenges identified by end-users.
The QSL puts applications in front and centre, fuelling the development of the field in the direction that is most useful societally.
We co-design new functionalities that solve practical problems with end users, and co-design applications that are aware of their technical limitations with hardware providers. In collaboration with the NQCC SparQ program we identify a wide variety of real-world use-cases that arise in different industry sectors. Moreover we translate industry desired figures of merit into concrete, measurable properties of the mathematical model to be developed for the problem.
Most known use cases fall into four archetypes: quantum simulation, quantum linear algebra for AI and machine learning, quantum optimisation and search, and quantum cybersecurity, with potential impacts in the following industrial sectors: pharmaceuticals, chemicals, automotive, and finance. We will explore these fields through individual case studies to identify gaps in the research landscape that must be addressed to ensure achievable quantum advantage.
Quantum Algorithms and Machine Learning
Quantum information processing is about finding algorithms and protocols that can solve problems more efficiently than their classical counterparts by storing and manipulating information within quantum systems.
The search for an application of near-term quantum devices is widespread and we investigate various design techniques (optimisation, machine learning, quantum annealing, etc) to deliver a specific task identified in our Application research considering the required quantum enhancement defined by the end-user’s desired figures of merit.
The aim is to determine which aspects of a use case could benefit from quantum support, helping us design the optimal quantum algorithm. Next, through complexity analysis and classical simulation of potential algorithms, we assess whether quantum advantage can be achieved for the desired requirements. In cases where it cannot, we develop novel quantum-inspired classical algorithms suitable for the HPC platform. Conversely, for cases where quantum advantage is possible, we advance the adaptation for suitable quantum hardware.
Quantum Cyber Security
Future information and communication networks will certainly consist of both classical and quantum devices, some of which are expected to be dishonest, with various degrees of functionality, ranging from simple routers to servers executing quantum algorithms.
Adopting a hybrid approach, which integrates quantum and classical elements, we explore various scenarios that span from the near-term post-quantum cryptography to the distant future of the quantum internet era. In particular we develop protocols for quantum cloud platforms that ensure the correctness, resilience, and trustworthiness of quantum computing by providing users with a secure, verifiable, and private environment for handling their data. We design practical solutions that can be deployed on currently available quantum cloud hardware platforms with multiple users using both quantum links as well hardware secure modules.
Programming and Implementation
This forms the central pillar of our activities, connecting our internal research with a broad array of external software and hardware partners, including ORCA, Rigetti, Riverlane, NPL, QCS, CISCO, and more.
Our hands-on development, facilitated through Hackathons and other code development events, provides early feedback for our ongoing research activities. This feedback helps us identify hardware limitations such as connectivity, native noise models, and native gates, which in turn guides the alignment of algorithms with specific quantum machines.
These efforts yield valuable insights into the constraints of current and emerging quantum software and hardware. This information informs the design of our target distributed software platform and accelerates our partners' collective development of quantum applications.
In collaboration with NQCC test-bed and all other hardware partners, we are developing and implementing a suite of verification and benchmarking tools tailored to current and imminent technology, to provide assurance to end users.
Our quantum advantage pathfinding investigation commenced with use-case discovery and evolved through the stages of design and implementation. It culminates in a comprehensive end-to-end feasibility study, which will provide a detailed, measurable, provable, and verifiable assessment of the quantum enhancement (if any at all) of the proposed solution in a scalable context for emerging quantum devices. Our approach encompasses a range of tools, including benchmarking, verification, and error characterization, mitigation and correction to assess and derive the best performance of different hardware platforms for specific applications.
Following the diagnosis of hardware bottlenecks, we develop new schemes and propose hardware designs aimed at enhancing the implementation's performance and, potentially, recalibrating the desired figures of merit based on our discoveries.
Architecture and HPC Integration
The long-term vision of QSL is the design of a network architecture of quantumly-enhanced hybrid devices, each possessing, alongside HPC units, a powerful Quantum Processor, that can quantumly access large amounts of data and perform computations that would have been impossible to perform with any classical computing machine.
The optimal performance for such a distributed design is achieved through a clear distinction between system and application programming, mirroring the established separation in traditional computer systems. Following the common practice in High-Performance Computing, we divide extensive circuits into smaller components, which are then distributed across various quantum processing units for parallel processing. These units are interconnected with only a few quantum connections, and the results are subsequently combined through circuit knitting. The quantum communication connector modules not only provide the essential solutions for scalability but also ensure the privacy and integrity of the overall computation through the adoption of the verifiable multi-clients blind quantum computing framework.