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SUMMARY:Simple quantum many-body dynamics reservoir for image classificati
 on
DTSTART;VALUE=DATE-TIME:20260908T122000Z
DTEND;VALUE=DATE-TIME:20260908T125000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191402Z
UID:indico-contribution-2027@events.ncbj.gov.pl
DESCRIPTION:Speakers: Akitada Sakurai (Okinawa institute of science and te
 chnology )\nRecently\, quantum machine learning\, particularly quantum res
 ervoir computing\, has attracted attention for directly utilizing the natu
 ral complex dynamics of quantum systems for information processing. It is 
 believed that by utilizing the vast internal space (Hilbert space) of quan
 tum systems\, performance comparable to classical models can be achieved w
 ith only a few qubits. However\, encoding classical information into a sma
 ll number of qubits makes it challenging to apply quantum models to large 
 inputs\, such as image classification. In this context\, in 2022\, we prop
 osed a new quantum model\, quantum extreme reservoir computing (QERC)\, th
 at combines classical lightweight compression techniques and achieves perf
 ormance comparable to classical models within the same machine learning fa
 mily. This talk presents the QERC model and the various quantum reservoir 
 systems we have experimented with. These include a wide range of possibili
 ties\, from using natural Hamiltonian dynamics\, as in the Ising model\, t
 o representing quantum circuits with random Clifford circuits. Furthermore
 \, this talk will address how much one can simplify the reservoir and how 
 much complexity is necessary\, which is of interest to both the theory and
  implementation sides.\n\nhttps://events.ncbj.gov.pl/event/468/contributio
 ns/2027/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2027/
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