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Details of Grant 

EPSRC Reference: EP/Y004590/1
Title: MACON-QC: Many-Body Phases In Continuous-Time Quantum Computation
Principal Investigator: Warburton, Professor PA
Other Investigators:
Bose, Professor S
Researcher Co-Investigators:
Project Partners:
Department: London Centre for Nanotechnology
Organisation: UCL
Scheme: Standard Research
Starts: 01 June 2023 Ends: 31 March 2025 Value (£): 551,255
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
25 Apr 2023 Software Enabled Quantum Computation Announced
Summary on Grant Application Form
Continuous-time approaches to quantum computation (QC) are fundamentally different from gate-based QC at all levels of the system stack, particularly at the software level. In general continuous-time approaches can be more robust with respect to decoherence, though at present the state of art of error-correction lags behind that of gate-based QC. Nevertheless the clear analogies between many-qubit continuous-time engineered quantum systems and condensed-matter systems in nature allow software designers to develop algorithms which are inspired by and draw on the vast knowledge of quantum phase-transitions in condensed-matter physics accumulated over the last century.

In MACON-QC we will develop continuous-time algorithms which are directly inspired by many-body phase transitions in condensed matter. These algorithms will utilise Hamiltonians which cannot currently be implemented in hardware, even at the two-qubit level. The ultimate goal of the research is to establish which qualitatively new hardware elements are required so as to enable future condensed-matter-inspired continuous-time quantum computers to demonstrate quantum speedup in real-world applications. In tandem with this task, approaches to certifying truly quantum behaviour during the continuous evolution will be developed so that it can be demonstrated that the algorithm goes through truly quantum states. Optimization of the newly-added elements and the algorithms via machine learning will also be explored.
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