EPSRC Reference: |
EP/Y004590/1 |
Title: |
MACON-QC: Many-Body Phases In Continuous-Time Quantum Computation |
Principal Investigator: |
Warburton, Professor PA |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
London Centre for Nanotechnology |
Organisation: |
UCL |
Scheme: |
Standard Research |
Starts: |
01 June 2023 |
Ends: |
31 March 2025 |
Value (£): |
551,255
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EPSRC Research Topic Classifications: |
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EPSRC Industrial Sector Classifications: |
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Related Grants: |
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Panel History: |
Panel Date | Panel Name | Outcome |
25 Apr 2023
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Software Enabled Quantum Computation
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Announced
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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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Key Findings |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Project URL: |
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Further Information: |
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Organisation Website: |
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