a new scheduling problem motivated by quantum computation
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A New Scheduling Problem Motivated by Quantum Computation. Robert Carr Anand Ganti Cynthia A. Phillips Sandia National Laboratories. Quantum Computation. Use a machine motivated by quantum mechanics to solve problems that are difficult for traditional computers - PowerPoint PPT PresentationTRANSCRIPT
Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company,for the United States Department of Energy’s National Nuclear Security Administration
under contract DE-AC04-94AL85000.
A New Scheduling Problem Motivated by Quantum
ComputationRobert CarrAnand Ganti
Cynthia A. PhillipsSandia National Laboratories
Slide 2
Quantum Computation
Use a machine motivated by quantum mechanics to solve problems that are difficult for traditional computers
Known benefits include faster:• Factoring• Search• Simulating quantum physics
To date, theoretical algorithms and a few early physical experiments
Slide 3
Sandia National Laboratories Project
• Sandia basic quantum information sciences– Advanced computing architectures– Future engineered systems will require increased
understanding of quantum effects.
• Current three-year project to– Build physical qubit
•Will test current understanding of quantum mechanics
– Design a logical qubit•There are scheduling problems critical for quantum
architecture design
Slide 4
Quantum Bits
• Classical bits: 0 or 1• Quantum bits (qubits):
• Superposition
• Measurement destroys superposition, makes
0 or 1
0 1
, complex numbers
2 2 1
2 * Probability of finding in state 0
2 * Probability of finding in state 1
0 or 1
Slide 5
Gates (examples)
1-bit gates:
2-bit gates:
preparation - create 0 or 1
X (not) : X 0 1 , X 1 0
Z : Z 0 0 , Z 1 1
Y : Y 0 i1 , Y 1 i 0measurement
swap : s xy yx
CNOT : c xy c x y x
if x 0 , y unchanged
if x 1 , y flipped
Slide 6
Quantum Errors
Interaction with environment decoherence
Errors act like X,Y,Z gates
Errors are continuous
X bit flip 0 1 1 0Z phase flip 0 1 0 1Y phase and bit flip
Slide 7
Quantum Error Correction
• Consider just flip errors• Idea similar to classical error correction
– Encode a single bit with more bits– Define a set of legal codewords– Ensure that all illegal codewords that result from a single
error are closest to unique legal codeword• Simple example:
• Use majority to correct any single flip error.• Real Example Steane [7,3,3], Calderbank-Shor-Steane codes
0 000
1 111
Slide 8
Quantum Complication 1
• Have to encode as without knowing or .
– Only 2 of the 8 possible states have positive probability• This circuit creates the appropriate (entangled) states:
0 1
000 111
0 1
0
0
000 111}
Slide 9
Quantum Complication 2
• Measurement destroys information• Ancilla bits
– Interact with real qbits– Pattern of ancilla values encodes single errors uniquely– Measure the ancilla
Slide 10
Quantum Error Correction
• Critical for quantum computing– Cannot completely isolate qubits from the world (e.g.
components of the computer itself)• Error correction happens often
– Essentially after every operation– Error correction vastly dominates operations
• Error correction is worth doing quickly/well– Throughput– Error threshold
• Burn error correction into silicon, kind of like microcode• The precise nature depends on
– General quantum architecture– Precise code
Slide 11
Our Architecture: Bilinear Array
Hollenberg et al
Rail
} Gate
Gate entry node
Gate node
Measurement Gate
= location that can hold a qubit/information
Slide 12
Bilinear Array: Legal Movement
• Move wherever there is an edge, including across gate• Multiple possible transport mechanisms such at CTAP (teleportation)• One edge per step (full to empty)• Bits cannot pass through each other
Slide 13
Error Correction is a Program
Three types of operations• Single bit• 2 bit• Measurement
PREPAREPLUS 7CNOT(7,9)MEASUREX 8MEASUREZ 9CNOT (0,3)CNOT (3,8)…
} Executed in gates
Slide 14
Scheduling Problem
• Select initial placement (cyclic)• Schedule location and timing of operations• Schedule legal movements• Obey precedence constraints
– (Usually) two operations that share a bit done serially• Possible parallelism limits
• Minimize makespan• Avoid unnecessary movement
Slide 15
Example
• 3 encoding bits, 2 ancilla• 4 measurements, 4 CNOTs (2-bit gates)
Slide 16
Example
m
Step 0
Slide 17
Example
Step 1m
CNOT
Slide 18
Example
Step 2
CNOT
Slide 19
Example
Step 3
Slide 20
Example
CNOT
Step 4
Slide 21
Example
CNOT
Step 5
Slide 22
Example
Step 6
Slide 23
Example
Step 7
Slide 24
Example
Step 8
m
m
Slide 25
Integer Programming Variables
• xbnt, binary, 1 if bit b in node n at start of time t• y(1)
git binary, 1 if 1-bit instruction i executes in gatenode g, time t
• y(2)git binary, 1 if 2-bit instruction i executes at full gate g,
time t• y(2f)
git same as y(2)git but flip control bit top to bottom
• y(m)mit binary, 1 if measurement instruction executes in
measurement gate m at time t• fbvwt implicit binary flow variables. Bit b moves v->w during
time t
Slide 26
Some simple Special Ordered Sets
• Bit locations (0 is empty)• Performing all operations
xbntn 1, b0,t
xbntb 1, n, t
ygit(m ) 1, i Im
gt
ygit(1)
gt 1, i I1
ygit(2) ygit
(2 f ) gt , i I2
Slide 27
Movement Control
• Flow conservation• Full->empty• Cyclic
fbuvtu,v E fbvvt xbv,t1 v, t
fbuvtu,v E fbuut xbut u, t
fbuvtb0u,v E
x0vt v, t
xbnT xbn1 b,n
Slide 28
Precedence Constraints
• 9 sets depending on i,j in I1, I2, Im• = minimum time between operations (usually 1)• Enforce only for nearest neighbors• EST = earliest start time• LAST = last start time
ygjEST j
t
g ygi
EST j
min(LAST (i),t )
g
i, j I1 : i j,EST j t LAST j
Slide 29
Matching Computation with Transportation
• ci = control bit• di = data bit• g1 = top gatenode of gate g• g2 = bottom gatenode of gate g
ygit(1) xd i gt g,i I1, t
ymit(m ) xd imt m,i Im,t
ygit(2) xci g1t
g,i I2, t
ygit(2) xd i g2t
g,i I2, t
ygit(2 f ) xd i g1t
g,i I2, t
ygit(2 f ) xci g2t
g,i I2,t
Slide 30
Stronger Transportation/Computation Coupling• If a bit is not in a gatenode at the proper time, none of the
associated gate-firing variables can be 1.
• Over 20x faster
(similar constraints for bottom gates and measurement gates)
ygit(1) ygit
(2)
g=ci
bd i
ygit(2 f )
gd i
xbgt b,g topgates, t
Slide 31
Objective
Generally none.Can add a relaxation variable z, relaxing all coupling
constraints:
Minimize z
Strange phenomenon: When z is integral, cplex 11 can require 4x as long to solve as when z and y’s are continuous.
When y’s are integral, having no z is better (tiny examples)
ygit(1) ygit
(2)
g=ci
bd i
ygit(2 f )
gd i
xbgt - z b,g topgates,t
Slide 32
LP cheating
• Half-bits can pass each other
m
m
CNOTCNOT
Steps 0 and 3 Steps 1 and 2
Slide 33
Comments and Issues
• LP example motivates forcing initial placements– Considerably faster– Have to enumerate over placements
•Need to understand structure• How to determine time? Number of rails
– Recursive doubling– Better to understand/compute bounds– LP time grows quickly with both
• Heuristics– LP based?– Constraint programming?
Slide 34
Extra Slides
Slide 35
Error Corrected Logical Qubit
Slide 36
Example
m
m
CNOT CNOT
Step 0 Step 1 Step 2
Slide 37
Example
CNOT CNOT
Step 3 Step 4 Step 5
Slide 38
Example
Step 6 Step 7 Step 8
m
m