Transcript ppt

Towards practical classical
processing for the surface code
Austin G. Fowler, Adam C. Whiteside, Lloyd C. L. Hollenberg
Centre for Quantum Computation and Communication Technology
School of Physics, The University of Melbourne, Australia
• why study the surface code?
• what is the surface code?
• classical processing challenges
– correcting errors fast enough
– interpreting logical measurements
• complexity optimal error processing
– performance in detail
– 3 second fault-tolerant distance 1000
• summary and further work
Why study the surface code?
• no known parallel arbitrary
interaction quantum computer
(QC) architecture
• many 2-D nearest neighbor
(NN) architectures
– ion traps, superconducting
circuits, quantum dots, NV
centers, neutral atoms, optical
lattices, electrons on helium, etc
• concatenated codes perform
poorly on 2-D NN architectures
– Steane, Bacon-Shor pth~2x10-5
– distance d = 9 Steane code
currently requires 2304 qubits
– d = 27 requires 100k+
– nq = d3.52
• surface code performs optimally
on a 2-D NN QC
• surface code has
– pth~10-2, so lower d required
– low overhead implementations
of the entire Clifford group
– flexible, long-range logical
– distance 9 surface code
currently requires 880 qubits
– distance 10 requires 1036
– distance 27 requires 4356
– nq = 6d2
– lower overhead and 10x
higher pth than any other
known 2-D topological code
The surface code is by far the
most promising and practical
code currently known.
Surface code: memory
a minimum of 13 qubits
(13 cells) are required
to create a single faulttolerant surface code
the nine circles store
the logical qubit, the
four dots are
repeatedly used to
detect errors on
neighboring qubits
an arbitrary single error
can be corrected,
multiple errors can be
corrected provided they
are separated in space
or time
in a full-scale surface
code quantum
computer, a minimum
of 72 qubits are
required per logical
the error correction
circuitry is simple and
transversely invariant
minimum-size logical qubit
detect X error
scalable logical qubit
detect Z error
order of interactions
Single logical qubit gates
when using error
correction, only a small set
of single logical qubit gates
are possible
for the surface code, these
are, in order of difficulty, X,
Z, H, S and T
X and Z are applied within
the classical control
H can be applied
transversely after cutting
the logical qubit out of the
S and T involve the
preparation of ancilla
states and gate
the ancilla states must be
distilled to achieve
sufficiently low probability
p of error
an efficient, nondestructive S circuit exists,
removing the need to
reinject and redistill Y
Hadamard after cutting
(stop interacting with qubits
outside the green square)
ancilla state required for S
ancilla state required for T
state distillation p → 7p3
gate teleportation S, T, RZ()
non-destructive S circuit
Surface code CNOT
there are two types of
surface code logical qubit,
rough and smooth
the simplest CNOT (CX)
has smooth control and
rough target
CX can be defined by its
effect on certain operators
given a state XI = ,
CX = CXXI =
IZ→ZZ defines CX
moving (braiding) the
holes (defects) that define
logical qubits deforms
their associated logical
operators, performing
movement is achieved by
dynamically changing
which data qubits are
error corrected
more complex braid
patterns are required for
rough-rough CX
smooth qubit
smooth-rough CNOT
rough qubit
CNOT braid patterns
Long-range CNOT
stretching the braid
pattern enables arbitrarily
long-range CNOT
the need to propagate
error correction
information near each
braid prevents violations
of relativity
Clifford group
computations can
proceed without waiting
for this information
this enables CNOTs to be
effectively bent
backwards in time
note that single control,
multiple target CNOT is a
simple braid pattern
arbitrary Clifford group
computations can be
performed in constant
Classical processing challenges
• correcting errors fast enough
– a single round of surface code
QEC can involve as few as
five sequential quantum gates
– depending on the underlying
technology, this may take less
than a microsecond
– need to be able to process a
complex, infinite size graph
problem in a very small
constant time
• interpreting logical
– surface code quantum
computing is essentially a
measurement based scheme
– logical gates, including the
identity gate, introduce
byproduct operators
– determining what these
byproduct operators are is
exceedingly difficult, especially
after optimizing a braid pattern
Correcting errors fast enough
consider the life cycle
of a single error
in the bulk of the
lattice, a single gate
error is always
detected at two
space-time points
given a detailed error
model for each gate,
the probability that
any given pair of
space-time points will
be connected by a
single error can be
these probabilities
can be represented
by two lattices of
recently completed
open source tool to
perform such error
analysis and
detect X error
detect Z error
Correcting errors fast enough
the primal (Z errors) and
dual (X errors) lattices are
deterministically constructed
terminology: dots and lines
weight of line –ln(pline)
stochastically detected
errors are represented by
vertices associated with
specific dots
edges between vertices
have weight equal to the
minimum weight path
through the lattice
by choosing a minimum
weight matching of vertices,
corrections can be applied
highly likely to preserve the
logical state
Correcting errors fast enough
minimum weight perfect matching
was invented by Jack Edmonds in
very well studied algorithm,
however best publicly available
implementations have complexity
O(n3) for complete graphs
don’t support continuous
don’t support parallel processing
actually quite slow
as recently as this year [PRA 83,
020302(R) (2011)], distance ~ 10
was the largest surface code that
had been studied fault-tolerantly
renormalization techniques have
been used to study large nonfault-tolerant surface codes [PRL
104, 050504 (2010),
need something much better if
surface code quantum computer is
to be built
Correcting errors fast enough
to describe our fast matching
algorithm, replace complex 3-D
lattice with simple uniform weight
2-D lattice (grey lines)
vertices (error chain endpoints)
are represented by black dots
matched edges are thick black
shaded regions are space-time
locations the algorithm has
Complexity optimal error correction
Complexity optimal error correction
choose a vertex
Complexity optimal error correction
explore local space-time region until other objects encountered
Complexity optimal error correction
if unmatched vertices encountered, match with one
Complexity optimal error correction
choose another vertex
Complexity optimal error correction
expand until other objects are encountered, build alternating tree
Complexity optimal error correction
it alternating tree outer space-time regions can’t be expanded, form blossom
Complexity optimal error correction
uniformly expand space-time region around blossom until other objects encountered
Complexity optimal error correction
two options in this case, unmatched vertex or boundary, choose vertex
Complexity optimal error correction
choose another vertex
Complexity optimal error correction
form alternating tree
Complexity optimal error correction
expand outer nodes, contract inner nodes
Complexity optimal error correction
form blossom
Complexity optimal error correction
grow alternating tree
Complexity optimal error correction
expand outer nodes, contract inner nodes, forbidden region entered
Complexity optimal error correction
undo expand outer, contract inner
Complexity optimal error correction
undo grow alternating tree
Complexity optimal error correction
undo form blossom
Complexity optimal error correction
undo expand outer, contract inner... done until have additional data
Complexity optimal error correction
noteworthy features:
– many rules, but each rule is
simple and cheap
– on average, each vertex only
needs local information
– fewer vertices = faster, simpler
and more local processing
– algorithm remains efficient at
and above threshold
average runtime is O(n) per
round of error correction
the runtime is independent of
the amount of history, which
can be discarded after a delay,
finite memory required
algorithm can be parallelized to
O(1) using constant computing
resources per unit area (unit
width in the example)
current implementation is
single processor only
Performance in detail
we simulate surface codes
of the form shown on the
depolarizing noise of
strength p is applied after
every unitary gate, including
the identity
measurement reports the
wrong eigenstate with
probability p but leaves the
state in the reported
for each d and p, our
simulations continuously
apply error correction until
10,000 logical errors have
been observed
the probability of logical
error per round of error
correction is then calculated
and plotted
Performance in detail
Performance in detail
matching, while still efficient, is much
slower around and above the
threshold error rate
very large blossoms appear
at d = 55, p = 10-2, approximately 6
GB of memory required
at an error rate p = 10-3, it is
straightforward to simulate d = 1000
over 4 million qubits
3 seconds per round of error
approximately 100 µs per vertex pair,
at least 90% of that is memory
access time
parallelized, this would be sufficiently
fast for many quantum computer
but not all...
achieving sufficiently fast, high error
rate, fault-tolerant, parallel surface
code quantum error correction is
extremely challenging
without further work, classical
processing speed will limit the
maximum tolerable error rate
Summary and further work
• complexity optimal algorithm
– O(n2) serial
– O(1) parallel
• accurately handle general error models
• sufficiently fast at p = 10-3 for many
• next 12 months:
– parallelize the algorithm
– benchmark on commercially available
parallel computing hardware
– reduce memory usage, improve core speed,
raise practical p
– take error correlations into account
– develop algorithms capable of simulating
braided logical gates
– get building!