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DTSTAMP:20220411T191004Z
UID:C025EF06-42EF-4196-A6D2-C784A615AD3E
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DESCRIPTION:Abstract. Programmable optical processors are promising structu
 res for ultrafast and energy efficient computation in classic and quantum 
 photonics. These processors efficiently perform the vector-matrix multipli
 cation extensively used in artificial intelligence and machine learning ta
 sks. Due to the inherent parallelism presents in optics in contrast with s
 equential operations in electronics\, optical processors offer better ener
 gy efficiency compared to their electronic counterparts. Today\, deep lear
 ning is facing growing computational demand limiting its development if we
  continue using conventional electronic processors. Energy efficient compu
 tational accelerators fabricated in silicon photonic are candidates to mee
 t the computational demands of future machine learning and deep learning t
 asks.\n\nProgrammable optical processors also pave the way for integrated 
 optical quantum computing. Single photons are excellent candidates for qua
 ntum computing due to their noise and decoherence-free nature. One can gen
 erate optical qubits by encoding single photons in one degree of freedom s
 uch as polarization or path. Optical integrated quantum computing requires
  quantum logic gates to manipulate these qubits. Quantum logic gates are r
 epresented by unitary matrices\, such that a 2n × 2n unitary matrix multi
 plication is identical to an n-qubit gate. Therefore\, a programmable opti
 cal processor capable of performing unitary matrix multiplication on singl
 e photons works as an arbitrary optical integrated quantum gate.\n\nSpeake
 r(s): Dr. Kaveh Mojaver\, \n\nRoom: TR0070\, Bldg: Lorne M. Trottier Engin
 eering Building\, McGill University\, 3630 University Street\, Montreal\, 
 Quebec\, Canada\, Virtual: https://events.vtools.ieee.org/m/310651
LOCATION:Room: TR0070\, Bldg: Lorne M. Trottier Engineering Building\, McGi
 ll University\, 3630 University Street\, Montreal\, Quebec\, Canada\, Virt
 ual: https://events.vtools.ieee.org/m/310651
ORGANIZER:odile@ieee.org
SEQUENCE:6
SUMMARY:Silicon Photonic Programmable Optical Processors for Machine Learni
 ng and Optical Quantum Computing
URL;VALUE=URI:https://events.vtools.ieee.org/m/310651
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Abstract. &lt;/em&gt;&lt;/strong&gt;Progra
 mmable optical processors are promising structures for ultrafast and energ
 y efficient computation in classic and quantum photonics. These processors
  efficiently perform the vector-matrix multiplication extensively used in 
 artificial intelligence and machine learning tasks. Due to the inherent pa
 rallelism presents in optics in contrast with sequential operations in ele
 ctronics\, optical processors offer better energy efficiency compared to t
 heir electronic counterparts. Today\, deep learning is facing growing comp
 utational demand limiting its development if we continue using conventiona
 l electronic processors. Energy efficient computational accelerators fabri
 cated in silicon photonic are candidates to meet the computational demands
  of future machine learning and deep learning tasks.&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;Progra
 mmable optical processors also pave the way for integrated optical quantum
  computing. Single photons are excellent candidates for quantum computing 
 due to their noise and decoherence-free nature. One can generate optical q
 ubits by encoding single photons in one degree of freedom such as polariza
 tion or path. Optical integrated quantum computing requires quantum logic 
 gates to manipulate these qubits. Quantum logic gates are represented by u
 nitary matrices\, such that a 2&lt;sup&gt;n&lt;/sup&gt; &amp;times\; 2&lt;sup&gt;n&lt;/sup&gt; unitary
  matrix multiplication is identical to an n-qubit gate. Therefore\, a prog
 rammable optical processor capable of performing unitary matrix multiplica
 tion on single photons works as an arbitrary optical integrated quantum ga
 te.&lt;/p&gt;
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